Reflect: Per-Project Self-Improvement

SkillAI & models

Per-project self-improvement - reads the .harness ledger and feedback memories, then proposes gated rule/threshold/ADR changes so the project stops repeating mistakes. Run periodically.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Reflect: Per-Project Self-Improvement skill

What this skill tells your AI

The instructions your AI receives, as published by sneg55/agent-starter in skills/reflect/SKILL.md and read by ahel’s review.

You are performing a reflection - turning the signal this project has captured into durable improvements. Nothing is applied without the developer's approval.

Phase 1 - Orient (gather signal)

  • Run the stats script over the ledger: ~/.claude/hooks/harness-ledger-stats.sh --ledger .harness/ledger.jsonl --min-recurr 3 If that path doesn't exist, the hooks aren't installed at ~/.claude/hooks/ - run the copy from wherever this project keeps them. If the script prints all zeros there is no ledger yet (no signal captured) - stop; there is nothing to reflect on.
  • Read the current project rules in CLAUDE.md so you improve them rather than duplicate.
  • Read recent feedback-type memory files - these are the developer's explicit corrections and are the highest-value signal. Locate the memory directory first (it sits next to MEMORY.md; find . -name MEMORY.md if unsure), then grep -l 'type: feedback' <memory-dir>/*.md.
  • Read the most recent .harness/reflections/*.md report (if any) to recall the last metric snapshot and what was already changed.

Phase 2 - Cluster

From the stats output and feedback memories, identify recurring problems:

  • Each recurring <rule> <prefix> <count> line is a friction cluster - the same check keeps firing in the same area.
  • Group related feedback corrections by theme.
  • Ignore one-off events; focus on what repeats.

Phase 3 - Propose (one candidate per cluster)

For each cluster, draft exactly one proposed change, choosing the fitting type:

TypeWhenWhere it lands
Project rulea convention would stop the repeatappend to CLAUDE.md project-specific section
Threshold changea guardrail is too strict/loosea diff to .claude/settings.json or the hook - shown, never auto-applied
Lint rulethe mistake is mechanically catchablea diff to eslint.config.mjs / biome.jsonc
ADR / knowledgedurable "why" worth keepinga new memory file or docs/adr/ note

Present all proposals together as a numbered list with the concrete change for each.

Phase 4 - Gate & Record

  • Ask the developer to approve, edit, or reject each proposal (like /remember).
  • Apply only the approved ones, then commit them (use /commit).
  • Create .harness/reflections/ if needed (mkdir -p .harness/reflections), then write a reflection report to .harness/reflections/YYYY-MM-DD.md containing:
    • the full stats output (the metric snapshot, so the next reflection can compare),
    • the clusters you found,
    • which proposals were approved / rejected and why.

The report is committed; the raw .harness/ledger.jsonl stays gitignored. Signal is private; wisdom is shared.

Measuring success

The headline metric is recurring_events from the stats output. Compare it to the value in the previous reflection report. If a rule you promoted last time worked, the cluster it targeted should have shrunk. Note the trend explicitly in the new report.

Signals

GitHub stars
77
Forks
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Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
reflect-sneg55
Source
github.com/sneg55/agent-starter